I have been running n8n for internal workflow automation for about six months. The short version: it is the least hyped and most useful piece of my personal tooling stack.
The setup: self-hosted on a modest container, connected to internal APIs, email, calendar, Slack-equivalent, and a few LLM endpoints. Nothing exotic.
What I learned:
The highest-value workflows are the ones that turn ambiguity into structure. The automation that forwards an email to a channel is fine. The workflow that ingests a messy inbound message, runs it through an LLM to extract structured fields, writes those fields to a database, and triggers a downstream decision is transformative. One of those workflows saved a colleague 6 hours a week.
LLM nodes should be used sparingly. Every workflow I have built with 3+ LLM nodes ended up flaky and expensive. Every workflow with 1 LLM node in a specific place, surrounded by deterministic logic, worked well. The LLM is a classifier or a summarizer; the rest is if-then.
Monitoring is non-negotiable. I learned this the expensive way. A workflow silently failed for three weeks before I noticed. Now every workflow writes success/failure signals to a dashboard, and I get pinged on failure patterns. Without this, you are building technical debt and calling it automation.
The biggest insight: workflow automation is a research practice, not just a productivity practice. The act of building a workflow forces me to articulate what a process actually does, which surfaces assumptions I did not know I was making. Half the value of automating something is the clarity you gain during the automation.
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